{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Financial analytics of S&P 500 and the VIX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gathering the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from alpha_vantage.timeseries import TimeSeries\n",
    "\n",
    "# Update your Alpha Vantage API key here...\n",
    "ALPHA_VANTAGE_API_KEY = 'PZ2ISG9CYY379KLI'\n",
    "\n",
    "ts = TimeSeries(key=ALPHA_VANTAGE_API_KEY, output_format='pandas')\n",
    "df_spx_data, meta_data = ts.get_daily_adjusted(\n",
    "    symbol='^GSPC', outputsize='full')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vix_data, meta_data = ts.get_daily_adjusted(\n",
    "    symbol='^VIX', outputsize='full')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4778 entries, 2000-01-03 to 2018-12-28\n",
      "Data columns (total 8 columns):\n",
      "1. open                 4778 non-null float64\n",
      "2. high                 4778 non-null float64\n",
      "3. low                  4778 non-null float64\n",
      "4. close                4778 non-null float64\n",
      "5. adjusted close       4778 non-null float64\n",
      "6. volume               4778 non-null float64\n",
      "7. dividend amount      4778 non-null float64\n",
      "8. split coefficient    4778 non-null float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 317.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_spx_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 4778 entries, 2000-01-03 to 2018-12-28\n",
      "Data columns (total 8 columns):\n",
      "1. open                 4778 non-null float64\n",
      "2. high                 4778 non-null float64\n",
      "3. low                  4778 non-null float64\n",
      "4. close                4778 non-null float64\n",
      "5. adjusted close       4778 non-null float64\n",
      "6. volume               4778 non-null float64\n",
      "7. dividend amount      4778 non-null float64\n",
      "8. split coefficient    4778 non-null float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 317.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_vix_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(columns=['SPX', 'VIX'])\n",
    "df['SPX'] = df_spx_data['5. adjusted close']\n",
    "df['VIX'] = df_vix_data['5. adjusted close']\n",
    "\n",
    "df.index = pd.to_datetime(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>VIX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>1455.22</td>\n",
       "      <td>24.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>1399.42</td>\n",
       "      <td>27.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>1402.11</td>\n",
       "      <td>26.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                SPX    VIX\n",
       "date                      \n",
       "2000-01-03  1455.22  24.21\n",
       "2000-01-04  1399.42  27.01\n",
       "2000-01-05  1402.11  26.41"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-03', '2000-01-04', '2000-01-05', '2000-01-06',\n",
       "               '2000-01-07', '2000-01-10', '2000-01-11', '2000-01-12',\n",
       "               '2000-01-13', '2000-01-14',\n",
       "               ...\n",
       "               '2018-12-14', '2018-12-17', '2018-12-18', '2018-12-19',\n",
       "               '2018-12-20', '2018-12-21', '2018-12-24', '2018-12-26',\n",
       "               '2018-12-27', '2018-12-28'],\n",
       "              dtype='datetime64[ns]', name='date', length=4778, freq=None)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performing analytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>VIX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4778.000000</td>\n",
       "      <td>4778.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1504.227518</td>\n",
       "      <td>19.702943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>510.521609</td>\n",
       "      <td>8.617418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>676.530000</td>\n",
       "      <td>9.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1142.195075</td>\n",
       "      <td>13.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1336.615000</td>\n",
       "      <td>17.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1862.692500</td>\n",
       "      <td>23.337500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2930.750000</td>\n",
       "      <td>80.860000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SPX          VIX\n",
       "count  4778.000000  4778.000000\n",
       "mean   1504.227518    19.702943\n",
       "std     510.521609     8.617418\n",
       "min     676.530000     9.140000\n",
       "25%    1142.195075    13.510000\n",
       "50%    1336.615000    17.580000\n",
       "75%    1862.692500    23.337500\n",
       "max    2930.750000    80.860000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 4778 entries, 2000-01-03 to 2018-12-28\n",
      "Data columns (total 2 columns):\n",
      "SPX    4778 non-null float64\n",
      "VIX    4778 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 112.0 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize = (12, 8))\n",
    "\n",
    "ax_spx = df['SPX'].plot()\n",
    "ax_vix = df['VIX'].plot(secondary_y=True)\n",
    "\n",
    "ax_spx.legend(loc=1)\n",
    "ax_vix.legend(loc=2)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.diff().hist(\n",
    "    figsize=(10, 5),\n",
    "    color='blue',\n",
    "    bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.pct_change().hist(\n",
    "    figsize=(10, 5),\n",
    "     color='blue',\n",
    "     bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "log_returns = np.log(df / df.shift(1)).dropna()\n",
    "log_returns.plot(\n",
    "    subplots=True,\n",
    "    figsize=(10, 8),\n",
    "    color='blue',\n",
    "    grid=True\n",
    ");\n",
    "for ax in plt.gcf().axes:\n",
    "    ax.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation between SPX and VIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SPX</th>\n",
       "      <th>VIX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SPX</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.733786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIX</th>\n",
       "      <td>-0.733786</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          SPX       VIX\n",
       "SPX  1.000000 -0.733786\n",
       "VIX -0.733786  1.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_returns.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "log_returns.plot(\n",
    "    figsize=(10,8),\n",
    "     x=\"SPX\",\n",
    "     y=\"VIX\",\n",
    "     kind='scatter')\n",
    "\n",
    "ols_fit = sm.OLS(log_returns['VIX'].values,\n",
    "log_returns['SPX'].values).fit()\n",
    "\n",
    "plt.plot(log_returns['SPX'], ols_fit.fittedvalues, 'r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ylabel('Rolling Annual Correlation')\n",
    "\n",
    "df_corr = df['SPX'].rolling(252).corr(other=df['VIX'])\n",
    "df_corr.plot(figsize=(12,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating the VIX index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv \n",
    "\n",
    "META_DATA_ROWS = 3  # Header data starts at line 4\n",
    "COLS = 7  # Each option data occupy 7 columns\n",
    "\n",
    "def read_file(filepath):\n",
    "    meta_rows = []\n",
    "    calls_and_puts = []\n",
    "\n",
    "    with open(filepath, 'r') as file:\n",
    "        reader = csv.reader(file)\n",
    "        for row, cells in enumerate(reader):\n",
    "            if row < META_DATA_ROWS:\n",
    "                meta_rows.append(cells)\n",
    "            else:\n",
    "                call = cells[:COLS]\n",
    "                put = cells[COLS:-1]\n",
    "\n",
    "                calls_and_puts.append((call, put))                        \n",
    "\n",
    "    return (meta_rows, calls_and_puts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "(meta_rows, calls_and_puts) = \\\n",
    "    read_file('files/chapter07/SPX_EOD_2018_10_15.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SPX (S&P 500 INDEX)', '2750.79', '-16.34']\n",
      "['Oct 15 2018 @ 20:00 ET']\n",
      "['Calls', 'Last Sale', 'Net', 'Bid', 'Ask', 'Vol', 'Open Int', 'Puts', 'Last Sale', 'Net', 'Bid', 'Ask', 'Vol', 'Open Int']\n"
     ]
    }
   ],
   "source": [
    "for line in meta_rows:\n",
    "    print(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dateutil import parser\n",
    "\n",
    "def get_dt_current(meta_rows):\n",
    "    \"\"\"\n",
    "    Extracts time information.\n",
    "\n",
    "    :param meta_rows: 2D array\n",
    "    :return: parsed datetime object\n",
    "    \"\"\"\n",
    "    # First cell of second row contains time info\n",
    "    date_time_row = meta_rows[1][0]\n",
    "\n",
    "    # Format text as ET time string\n",
    "    current_time = date_time_row.strip()\\\n",
    "        .replace('@ ', '')\\\n",
    "        .replace('ET', '-05:00')\\\n",
    "        .replace(',', '')\n",
    "\n",
    "    dt_current =  parser.parse(current_time)\n",
    "    return dt_current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-10-15 20:00:00-05:00\n"
     ]
    }
   ],
   "source": [
    "dt_current =  get_dt_current(meta_rows)\n",
    "print(dt_current)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(['2018 Oct 15 1700.00 (SPXW1815J1700)', '0.0', '0.0', '1039.30', '1063.00', '0', '0'], ['2018 Oct 15 1700.00 (SPXW1815V1700)', '0.15', '0.0', ' ', '0.05', '0'])\n",
      "(['2018 Oct 15 1800.00 (SPXW1815J1800)', '0.0', '0.0', '939.40', '963.00', '0', '0'], ['2018 Oct 15 1800.00 (SPXW1815V1800)', '0.10', '0.0', ' ', '0.05', '0'])\n"
     ]
    }
   ],
   "source": [
    "for line in calls_and_puts[:2]:\n",
    "    print(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from decimal import Decimal\n",
    "\n",
    "def parse_expiry_and_strike(text):\n",
    "    \"\"\"\n",
    "    Extracts information about the contract data.\n",
    "\n",
    "    :param text: the string to parse.\n",
    "    :return: a tuple of expiry date and strike price\n",
    "    \"\"\"\n",
    "    # SPX Weeklys should expire at 3PM Chicago time.\n",
    "    [year, month, day, strike, option_code] = text.split(' ')\n",
    "    expiry = '%s %s %s 3:00PM -05:00' % (year, month, day)\n",
    "    dt_object = parser.parse(expiry)    \n",
    "\n",
    "    \"\"\"\n",
    "    Third friday SPX standard options expire at start of trading\n",
    "    8.30 A.M. Chicago time.\n",
    "    \"\"\"\n",
    "    if is_third_friday(dt_object):\n",
    "        dt_object = dt_object.replace(hour=8, minute=30)\n",
    "\n",
    "    strike = Decimal(strike)    \n",
    "    return (dt_object, strike)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_third_friday(dt_object):\n",
    "    return dt_object.weekday() == 4 and 15 <= dt_object.day <= 21"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing our function with a simple contract code data and printing the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_contract_code = '2018 Sep 26 1800.00 (*)'\n",
    "(expiry, strike) = parse_expiry_and_strike(test_contract_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expiry: 2018-09-26 15:00:00-05:00\n",
      "Strike price: 1800.00\n"
     ]
    }
   ],
   "source": [
    "print('Expiry:', expiry)\n",
    "print('Strike price:', strike)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time round, let's test our function with a contract code data that falls on a third Friday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_contract_code = '2018 Oct 19 2555.00 (*)'\n",
    "(expiry, strike) = parse_expiry_and_strike(test_contract_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expiry: 2018-10-19 08:30:00-05:00\n",
      "Strike price: 2555.00\n"
     ]
    }
   ],
   "source": [
    "print('Expiry:', expiry)\n",
    "print('Strike price:', strike)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our utility function in place, we can now go ahead and parse a single call or put option price entry, and return useful information which we can use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_option_data(option_data):\n",
    "    [desc, _, _, bid_str, ask_str] = option_data[:5]\n",
    "    bid = Decimal(bid_str.strip() or '0')\n",
    "    ask = Decimal(ask_str.strip() or '0')\n",
    "    mid = (bid+ask) / Decimal(2)\n",
    "    (expiry, strike) = parse_expiry_and_strike(desc)\n",
    "    return (expiry, strike, bid, ask, mid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding near-term and next-term options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "CALL_COLS = ['call_bid', 'call_ask', 'call_mid']\n",
    "PUT_COLS = ['put_bid', 'put_ask', 'put_mid']\n",
    "COLUMNS = CALL_COLS + PUT_COLS + ['diff']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def generate_options_chain(calls_and_puts):\n",
    "    chain = {}\n",
    "\n",
    "    for row in calls_and_puts:\n",
    "        (call, put) = row\n",
    "\n",
    "        (call_expiry, call_strike, call_bid, call_ask, call_mid) = \\\n",
    "            format_option_data(call)\n",
    "        (put_expiry, put_strike, put_bid, put_ask, put_mid) = \\\n",
    "            format_option_data(put)\n",
    "\n",
    "        # Ensure each line contains the same put and call maturity\n",
    "        assert(call_expiry == put_expiry)\n",
    "\n",
    "        # Get or create the DataFrame at the expiry\n",
    "        df = chain.get(call_expiry, pd.DataFrame(columns=COLUMNS))\n",
    "\n",
    "        df.loc[call_strike, CALL_COLS] = \\\n",
    "            [call_bid, call_ask, call_mid]\n",
    "        df.loc[call_strike, PUT_COLS] = \\\n",
    "            [put_bid, put_ask, put_mid]\n",
    "        df.loc[call_strike, 'diff'] = abs(put_mid-call_mid)\n",
    "\n",
    "        chain[call_expiry] = df\n",
    "\n",
    "    return chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = generate_options_chain(calls_and_puts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-10-15 15:00:00-05:00\n",
      "2018-10-17 15:00:00-05:00\n",
      "...\n",
      "2020-06-19 08:30:00-05:00\n",
      "2020-12-18 08:30:00-05:00\n"
     ]
    }
   ],
   "source": [
    "chain_keys = list(chain.keys())\n",
    "for row in chain_keys[:2]:\n",
    "    print(row)\n",
    "print('...')\n",
    "for row in chain_keys[-2:]:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_option_terms(chain, dt_current):\n",
    "    \"\"\"\n",
    "    Find the near-term and next-term dates from\n",
    "    the given indexes of the dictionary.\n",
    "\n",
    "    :param chain: dictionary object\n",
    "    :param dt_current: DateTime object of option quotes\n",
    "    :return: tuple of 2 datetime objects\n",
    "    \"\"\"\n",
    "    dt_near = None\n",
    "    dt_next = None\n",
    "\n",
    "    for dt_object in chain.keys():\n",
    "        delta = dt_object - dt_current\n",
    "        if delta.days > 23:\n",
    "            # Skip non-fridays\n",
    "            if dt_object.weekday() != 4:\n",
    "                continue\n",
    "\n",
    "            # Save the near term date\n",
    "            if dt_near is None:\n",
    "                dt_near = dt_object            \n",
    "                continue\n",
    "\n",
    "            # Save the next term date\n",
    "            if dt_next is None:\n",
    "                dt_next = dt_object            \n",
    "                break\n",
    "\n",
    "    return (dt_near, dt_next)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "(dt_near, dt_next) = find_option_terms(chain, dt_current)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found near-term maturity 2018-11-09 15:00:00-05:00 with 24 days, 19:00:00 to expiry\n",
      "Found next-term maturity 2018-11-16 08:30:00-05:00 with 31 days, 12:30:00 to expiry\n"
     ]
    }
   ],
   "source": [
    "print('Found near-term maturity', dt_near, \n",
    "      'with', dt_near-dt_current, 'to expiry')\n",
    "print('Found next-term maturity', dt_next, \n",
    "      'with', dt_next-dt_current, 'to expiry')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the required minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_start_year = dt_current.replace(\n",
    "    month=1, day=1, hour=0, minute=0, second=0)\n",
    "dt_end_year = dt_start_year.replace(year=dt_current.year+1)\n",
    "\n",
    "N_t1 = Decimal((dt_near-dt_current).total_seconds() // 60)\n",
    "N_t2 = Decimal((dt_next-dt_current).total_seconds() // 60)\n",
    "N_30 = Decimal(30 * 24 * 60)\n",
    "N_365 = Decimal((dt_end_year-dt_start_year).total_seconds() // 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N_365: 525600\n",
      "N_30: 43200\n",
      "N_t1: 35700\n",
      "N_t2: 45390\n"
     ]
    }
   ],
   "source": [
    "print('N_365:', N_365)\n",
    "print('N_30:', N_30)\n",
    "print('N_t1:', N_t1)\n",
    "print('N_t2:', N_t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = N_t1 / N_365\n",
    "t2 = N_t2 / N_365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t1:0.06792\n",
      "t2:0.08636\n"
     ]
    }
   ],
   "source": [
    "print('t1:%.5f'%t1)\n",
    "print('t2:%.5f'%t2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the forward SPX index level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def determine_forward_level(df, r, t):\n",
    "    \"\"\"\n",
    "    Calculate the forward SPX index level.\n",
    "\n",
    "    :param df: pandas DataFrame for a single option chain\n",
    "    :param r: risk-free interest rate for t\n",
    "    :param t: time to settlement in years\n",
    "    :return: Decimal object\n",
    "    \"\"\"\n",
    "    min_diff = min(df['diff'])\n",
    "    pd_k = df[df['diff'] == min_diff]\n",
    "    k = pd_k.index.values[0]\n",
    "\n",
    "    call_price = pd_k.loc[k, 'call_mid']\n",
    "    put_price = pd_k.loc[k, 'put_mid']\n",
    "    return k + Decimal(math.exp(r*t))*(call_price-put_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = Decimal(2.17/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_near = chain.get(dt_near)\n",
    "f1 = determine_forward_level(df_near, r, t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1: 2747.596459994546094129930225\n"
     ]
    }
   ],
   "source": [
    "print('f1:', f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding the required forward strike prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_k0(df, f):\n",
    "    return df[df.index<f].tail(1).index.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "k0_near = find_k0(df_near, f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k0_near: 2745.00\n"
     ]
    }
   ],
   "source": [
    "print('k0_near:', k0_near)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determining strike price boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_lower_and_upper_bounds(df, k0):\n",
    "    \"\"\"\n",
    "    Find the lower and upper boundry strike prices.\n",
    "\n",
    "    :param df: the pandas DataFrame of option chain\n",
    "    :param k0: the forward strike price\n",
    "    :return: a tuple of two Decimal objects\n",
    "    \"\"\"\n",
    "    # Find lower bound\n",
    "    otm_puts = df[df.index<k0].filter(['put_bid', 'put_ask'])\n",
    "    k_lower = 0\n",
    "    for i, k in enumerate(otm_puts.index[::-1][:-2]):\n",
    "        k_lower = k\n",
    "        put_bid_t1 = otm_puts.iloc[-i-1-1]['put_bid']\n",
    "        put_bid_t2 = otm_puts.iloc[-i-1-2]['put_bid']\n",
    "        if put_bid_t1 == 0 and put_bid_t2 == 0:\n",
    "            break\n",
    "        if put_bid_t2 == 0:\n",
    "            k_lower = otm_puts.index[-i-1-1]\n",
    "\n",
    "    # Find upper bound\n",
    "    otm_calls = df[df.index>k0].filter(['call_bid', 'call_ask'])    \n",
    "    k_upper = 0\n",
    "    for i, k in enumerate(otm_calls.index[:-2]):\n",
    "        call_bid_t1 = otm_calls.iloc[i+1]['call_bid']\n",
    "        call_bid_t2 = otm_calls.iloc[i+2]['call_bid']\n",
    "        if call_bid_t1 == 0 and call_bid_t2 == 0:\n",
    "            k_upper = k\n",
    "            break\n",
    "\n",
    "    return (k_lower, k_upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "(k_lower_near, k_upper_near) = \\\n",
    "    find_lower_and_upper_bounds(df_near, k0_near)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1250.00 3040.00\n"
     ]
    }
   ],
   "source": [
    "print(k_lower_near, k_upper_near)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tabulating contributions by strike prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_contrib_by_strike(delta_k, k, r, t, q):\n",
    "    return (delta_k / k**2)*Decimal(math.exp(r*t))*q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_prev_k(k, i, k_lower, df, bid_column):\n",
    "    \"\"\"\n",
    "    Finds the strike price immediately below k \n",
    "    with non-zero bid.\n",
    "\n",
    "    :param k: current strike price at i\n",
    "    :param i: current index of df\n",
    "    :param k_lower: lower strike price boundary of df\n",
    "    :param bid_column: The column name that reads the bid price.\n",
    "        Can be 'put_bid' or 'call_bid'.\n",
    "    :return: strike price as Decimal object.\n",
    "    \"\"\"    \n",
    "    if k <= k_lower:\n",
    "        k_prev = df.index[i-1]\n",
    "        return k_prev\n",
    "\n",
    "    # Iterate backwards to find put bids           \n",
    "    k_prev = 0\n",
    "    prev_bid = 0\n",
    "    steps = 1\n",
    "    while prev_bid == 0:                                \n",
    "        k_prev = df.index[i-steps]\n",
    "        prev_bid = df.loc[k_prev][bid_column]\n",
    "        steps += 1\n",
    "\n",
    "    return k_prev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_next_k(k, i, k_upper, df, bid_column):\n",
    "    \"\"\"\n",
    "    Finds the strike price immediately above k \n",
    "    with non-zero bid.\n",
    "\n",
    "    :param k: current strike price at i\n",
    "    :param i: current index of df\n",
    "    :param k_upper: upper strike price boundary of df\n",
    "    :param bid_column: The column name that reads the bid price.\n",
    "        Can be 'put_bid' or 'call_bid'.\n",
    "    :return: strike price as Decimal object.\n",
    "    \"\"\"    \n",
    "    if k >= k_upper:\n",
    "        k_next = df.index[i+1]\n",
    "        return k_next\n",
    "\n",
    "    k_next = 0\n",
    "    next_bid = 0\n",
    "    steps = 1\n",
    "    while next_bid == 0:\n",
    "        k_next = df.index[i+steps]\n",
    "        next_bid = df.loc[k_next][bid_column]\n",
    "        steps += 1\n",
    "\n",
    "    return k_next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def tabulate_contrib_by_strike(df, k0, k_lower, k_upper, r, t):\n",
    "    \"\"\"\n",
    "    Computes the contribution to the VIX index\n",
    "    for every strike price in df.\n",
    "\n",
    "    :param df: pandas DataFrame containing the option dataset\n",
    "    :param k0: forward strike price index level\n",
    "    :param k_lower: lower boundary strike price\n",
    "    :param k_upper: upper boundary strike price\n",
    "    :param r: the risk-free interest rate\n",
    "    :param t: the time to expiry, in years\n",
    "    :return: new pandas DataFrame with contributions by strike price\n",
    "    \"\"\"\n",
    "    COLUMNS = ['Option Type', 'mid', 'contrib']\n",
    "    pd_contrib = pd.DataFrame(columns=COLUMNS)\n",
    "\n",
    "    for i, k in enumerate(df.index):\n",
    "        mid, bid, bid_column = 0, 0, ''\n",
    "        if k_lower <= k < k0:\n",
    "            option_type = 'Put'\n",
    "            bid_column = 'put_bid'\n",
    "            mid = df.loc[k]['put_mid']\n",
    "            bid = df.loc[k][bid_column]\n",
    "        elif k == k0:\n",
    "            option_type = 'atm'\n",
    "        elif k0 < k <= k_upper:\n",
    "            option_type = 'Call'\n",
    "            bid_column = 'call_bid'\n",
    "            mid = df.loc[k]['call_mid']\n",
    "            bid = df.loc[k][bid_column]\n",
    "        else:\n",
    "            continue  # skip out-of-range strike prices\n",
    "\n",
    "        if bid == 0:\n",
    "            continue  # skip zero bids\n",
    "\n",
    "        k_prev = find_prev_k(k, i, k_lower, df, bid_column)\n",
    "        k_next = find_next_k(k, i, k_upper, df, bid_column)\n",
    "        delta_k = Decimal((k_next-k_prev)/2)\n",
    "\n",
    "        contrib = calculate_contrib_by_strike(delta_k, k, r, t, mid)\n",
    "        pd_contrib.loc[k, COLUMNS] = [option_type, mid, contrib]\n",
    "\n",
    "    return pd_contrib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_contrib_near = tabulate_contrib_by_strike(\n",
    "    df_near, k0_near, k_lower_near, k_upper_near, r, t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Option Type</th>\n",
       "      <th>mid</th>\n",
       "      <th>contrib</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1250.00</th>\n",
       "      <td>Put</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.000003204720007271874493426366826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300.00</th>\n",
       "      <td>Put</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.000003703679742131881579865901010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1350.00</th>\n",
       "      <td>Put</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.000004121296305647986745661479970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400.00</th>\n",
       "      <td>Put</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000005109566338124799893855814454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450.00</th>\n",
       "      <td>Put</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.000004763258036967708819004706934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Option Type    mid                              contrib\n",
       "1250.00         Put   0.10  0.000003204720007271874493426366826\n",
       "1300.00         Put  0.125  0.000003703679742131881579865901010\n",
       "1350.00         Put   0.15  0.000004121296305647986745661479970\n",
       "1400.00         Put   0.20  0.000005109566338124799893855814454\n",
       "1450.00         Put   0.20  0.000004763258036967708819004706934"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_contrib_near.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Option Type</th>\n",
       "      <th>mid</th>\n",
       "      <th>contrib</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3020.00</th>\n",
       "      <td>Call</td>\n",
       "      <td>0.175</td>\n",
       "      <td>9.608028452572290489411343569E-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025.00</th>\n",
       "      <td>Call</td>\n",
       "      <td>0.225</td>\n",
       "      <td>1.231237623174939828257858985E-7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3030.00</th>\n",
       "      <td>Call</td>\n",
       "      <td>0.175</td>\n",
       "      <td>9.544713775211615220689389699E-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3035.00</th>\n",
       "      <td>Call</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.087233242345573774601901086E-7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3040.00</th>\n",
       "      <td>Call</td>\n",
       "      <td>0.15</td>\n",
       "      <td>8.127448187590304540304760266E-8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Option Type    mid                           contrib\n",
       "3020.00        Call  0.175  9.608028452572290489411343569E-8\n",
       "3025.00        Call  0.225  1.231237623174939828257858985E-7\n",
       "3030.00        Call  0.175  9.544713775211615220689389699E-8\n",
       "3035.00        Call   0.20  1.087233242345573774601901086E-7\n",
       "3040.00        Call   0.15  8.127448187590304540304760266E-8"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_contrib_near.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the volatilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_volatility(pd_contrib, t, f, k0):\n",
    "    \"\"\"\n",
    "    Calculate the volatility for a single-term option\n",
    "\n",
    "    :param pd_contrib: pandas DataFrame containing \n",
    "        contributions by strike\n",
    "    :param t: time to settlement of the option\n",
    "    :param f: forward index level\n",
    "    :param k0: immediate strike price below the forward level\n",
    "    :return: volatility as Decimal object\n",
    "    \"\"\"\n",
    "    term_1 = Decimal(2/t)*pd_contrib['contrib'].sum()\n",
    "    term_2 = Decimal(1/t)*(f/k0 - 1)**2\n",
    "    return term_1 - term_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "volatility_near = calculate_volatility(\n",
    "    pd_contrib_near, t1, f1, k0_near)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "volatility_near: 0.04891704334249740486501736967\n"
     ]
    }
   ],
   "source": [
    "print('volatility_near:', volatility_near)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the next-term options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_next = chain.get(dt_next)\n",
    "\n",
    "f2 = determine_forward_level(df_next, r, t2)\n",
    "k0_next = find_k0(df_next, f2)\n",
    "(k_lower_next, k_upper_next) = \\\n",
    "    find_lower_and_upper_bounds(df_next, k0_next)\n",
    "pd_contrib_next = tabulate_contrib_by_strike(\n",
    "    df_next, k0_next, k_lower_next, k_upper_next, r, t2)\n",
    "volatility_next = calculate_volatility(\n",
    "    pd_contrib_next, t2, f2, k0_next)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the VIX index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_vix_index(t1, volatility_1, t2, \n",
    "                        volatility_2, N_t1, N_t2, N_30, N_365):\n",
    "    inner_term_1 = t1*Decimal(volatility_1)*(N_t2-N_30)/(N_t2-N_t1)\n",
    "    inner_term_2 = t2*Decimal(volatility_2)*(N_30-N_t1)/(N_t2-N_t1)\n",
    "    sqrt_terms = math.sqrt((inner_term_1+inner_term_2)*N_365/N_30)\n",
    "    return 100 * sqrt_terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "vix = calculate_vix_index(\n",
    "    t1, volatility_near, t2, \n",
    "    volatility_next, N_t1, N_t2, \n",
    "    N_30, N_365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At 2018-10-15 20:00:00-05:00 the VIX is 21.431114075693934\n"
     ]
    }
   ],
   "source": [
    "print('At', dt_current, 'the VIX is', vix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating multiple VIX indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "    def process_file(filepath):\n",
    "        \"\"\"\n",
    "        Reads the filepath and calculates the VIX index.\n",
    "\n",
    "        :param filepath: path the options chain file\n",
    "        :return: VIX index value\n",
    "        \"\"\"\n",
    "        headers, calls_and_puts = read_file(filepath)    \n",
    "        dt_current = get_dt_current(headers)\n",
    "\n",
    "        chain = generate_options_chain(calls_and_puts)\n",
    "        (dt_near, dt_next) = find_option_terms(chain, dt_current)\n",
    "\n",
    "        N_t1 = Decimal((dt_near-dt_current).total_seconds() // 60)\n",
    "        N_t2 = Decimal((dt_next-dt_current).total_seconds() // 60)\n",
    "        t1 = N_t1 / N_365\n",
    "        t2 = N_t2 / N_365\n",
    "\n",
    "        # Process near-term options\n",
    "        df_near = chain.get(dt_near)\n",
    "        f1 = determine_forward_level(df_near, r, t1)\n",
    "        k0_near = find_k0(df_near, f1)\n",
    "        (k_lower_near, k_upper_near) = find_lower_and_upper_bounds(\n",
    "            df_near, k0_near)\n",
    "        pd_contrib_near = tabulate_contrib_by_strike(\n",
    "            df_near, k0_near, k_lower_near, k_upper_near, r, t1)\n",
    "        volatility_near = calculate_volatility(\n",
    "            pd_contrib_near, t1, f1, k0_near)\n",
    "\n",
    "        # Process next-term options\n",
    "        df_next = chain.get(dt_next)\n",
    "        f2 = determine_forward_level(df_next, r, t2)\n",
    "        k0_next = find_k0(df_next, f2)\n",
    "        (k_lower_next, k_upper_next) = find_lower_and_upper_bounds(\n",
    "            df_next, k0_next)\n",
    "        pd_contrib_next = tabulate_contrib_by_strike(\n",
    "            df_next, k0_next, k_lower_next, k_upper_next, r, t2)\n",
    "        volatility_next = calculate_volatility(\n",
    "            pd_contrib_next, t2, f2, k0_next)\n",
    "\n",
    "        vix = calculate_vix_index(\n",
    "            t1, volatility_near, t2, \n",
    "            volatility_next, N_t1, N_t2, \n",
    "            N_30, N_365)\n",
    "\n",
    "        return vix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "FILE_DATES = [\n",
    "    '2018_10_15',\n",
    "    '2018_10_16',\n",
    "    '2018_10_17',\n",
    "    '2018_10_18',\n",
    "    '2018_10_19',\n",
    "]\n",
    "FILE_PATH_PATTERN = 'files/chapter07/SPX_EOD_%s.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_calcs = pd.DataFrame(columns=['VIX'])\n",
    "\n",
    "for file_date in FILE_DATES:\n",
    "    filepath = FILE_PATH_PATTERN % file_date\n",
    "\n",
    "    vix = process_file(filepath)    \n",
    "    date_obj = parser.parse(file_date.replace('_', '-'))\n",
    "\n",
    "    pd_calcs.loc[date_obj, 'VIX'] = vix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-15</th>\n",
       "      <td>21.4311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-16</th>\n",
       "      <td>17.7384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-17</th>\n",
       "      <td>17.4741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-18</th>\n",
       "      <td>20.0477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-19</th>\n",
       "      <td>19.9196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                VIX\n",
       "2018-10-15  21.4311\n",
       "2018-10-16  17.7384\n",
       "2018-10-17  17.4741\n",
       "2018-10-18  20.0477\n",
       "2018-10-19  19.9196"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_calcs.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vix = df_vix_data['2018-10-14':'2018-10-21']['5. adjusted close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2018-10-15    21.30\n",
       "2018-10-16    17.62\n",
       "2018-10-17    17.40\n",
       "2018-10-18    20.06\n",
       "2018-10-19    19.89\n",
       "Name: 5. adjusted close, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vix.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EWrWgXz/497/h009DpxEREZFEpqKrknr3hr32gnvuCZ1EREREEpmKrkpq0ACuvhpefhm++SZ0GhEREUlUKrqqwPXX+67G4cNDJxEREZFEpaKrCmRlwaWXwjPPwJKoK0uKiIhIulPRVUVuvBG2bPFrMoqIiIjsSkVXFTngADjvPHjsMfj559BpREREJNGo6KpCubmwZg08+mjoJCIiIpJoVHRVoY4d4bTTfBfj+vWh04iIiEgiUdFVxQYOhOXL4emnQycRERGRRKKiq4qdeCJ07uynj9iyJXQaERERSRQquqqYmW/t+v57mDgxdBoRERFJFCq6YuCcc6BtW78QtnOh04iIiEgiUNEVA9WqwYABMH06vPNO6DQiIiKSCFR0xUiPHtC0qW/tEhEREVHRFSO1asENN8DkyTB1aug0IiIiEpqKrhjq3Rv22gvuuSd0EhEREQlNRVcM1a8P11wDkybBnDmh04iIiEhIKrpi7PrrfVfj8OGhk4iIiEhIKrpibL/94LLL4JlnYPHi0GlEREQkFBVdcXDjjVBU5NdkFBERkfSkoisOWreG886Dxx6Dn34KnUZERERCUNEVJ7m5UFgIjz4aOomIiIiEoKIrTg4/HE4/3Xcxrl8fOo2IiIjEW6lFl5m1MLPJZjbLzL4ysz6R7Y3N7B0zmxu5blTC8VvNbHrk8lpVv4FkMnAgrFgB48aFTiIiIiLxVpaWri1Af+dcO+AY4BozOwQYCLznnDsIeC9yP5r1zrmOkcvZVZI6SZ1wAhx9tJ8+YsuW0GlEREQknkotupxzS51zn0VurwFmAc2Ac4CnI7s9Dfw2ViFThZlv7frhB3jppdBpREREJJ4yyrOzmWUDnYBPgCzn3FLwhZmZ7VfCYbXNrADfYjbUOfdKCc/dG+gNkJWVRX5+fnmiJY0GDaBly6P4618dWVkFmIVOJCIikpoKCwsTqp4oc9FlZvWAiUBf59wvVvZqoaVzbomZHQD828y+dM59t+tOzrkxwBiAnJwc17Vr17I+f9K5/XY/YeqmTV057bTQaURERFJTfn4+iVRPlOnsRTOrgS+48pxzL0c2LzOz/SOP7w8sj3asc25J5Pp7IB/fUpbWevSAZs1g6NDQSURERCReynL2ogFPArOcc/cVe+g1oGfkdk/g1SjHNjKzWpHb+wDHAl9XNnSyq1kTbrgB8vPhk09CpxEREZF4KEtL17HARcDJxaZ+6A4MBX5jZnOB30TuY2Y5ZvZE5Nh2QIGZfQFMxo/pSvuiC6BXL9hrL7jnntBJREREJB5KHdPlnPsQKGkA1ylR9i8A/hK5/V+gfWUCpqr69eHaa2HIEJg9Gw4+OHQiERERiSXNSB/QdddBrVp+3i4RERFJbSq6AtpvP7j8cvj732Hx4tBpREREJJZUdAXWvz8UFcH994dOIiIiIrGkoiuw1q3h/PNh9Gj46afQaURERCRWVHQlgNxcKCyERx4JnURERERiRUVXAujQAc44Ax58ENavD51GREREYkFFV4IYOBBWrICnngqdRERERGJBRVeCOP54OOYYGDECtmwJnUZERESqmoquBGHmW7t++AFefDF0GhEREalqKroSyP/9H7Rr55cGci50GhEREalKKroSSLVqMGAAfPEFvPVW6DQiIiJSlVR0JZg//QmaN4ehQ0MnERERkaqkoivB1KwJN9wA778PH38cOo2IiIhUFRVdCahXL2jUyI/tEhERkdSgoisB1asH114Lr7wCs2aFTiMiIiJVQUVXgrruOqhTB4YPD51EREREqoKKrgS1775w+eXw7LOwaFHoNCIiIlJZKroSWP/+UFQE998fOomIiIhUloquBJadDRdcAGPGwKpVodOIiIhIZajoSnC5uVBYCI88EjqJiIiIVIaKrgTXvj107w4jR8K6daHTiIiISEWp6EoCAwfCihXw1FOhk4iIiEhFqehKAscdB7/+NYwYAVu2hE4jIiIiFaGiKwmY+dauefPghRdCpxEREZGKUNGVJM46Cw45xC8N5FzoNCIiSSAvz58GXq2av87LC51I0pyKriRRrRoMGAAzZsCbb4ZOIyKS4PLyoHdvmD/ff1OdP9/fV+ElAanoSiIXXgjNm2shbBGRUg0evPsp3+vW+e0igWSEDiBlV7Omn6W+Xz/46CM/uF5ERKJYsCDq5qL5C+hwGOy9d9kujRtDhv6nlCpS6j8lM2sBPAM0AYqAMc65B82sMTAByAbmAec5536KcnxP4K+Ru3c5556umujp6S9/gTvv9K1dr7wSOo2ISIJq2dJ3Ke5iVd2WHHQQrFwJc+b465Ur93xmeMOGOxdi++xTerGWmelPghIpriz1+xagv3PuMzOrD0wzs3eAS4D3nHNDzWwgMBDILX5gpDC7DcgBXOTY16IVZ1I29erBddf5wmvWLGjXLnQiEZHEU3TXEDb27E2domJdjJmZ7DN6CJN67Lyvc7BmzY4CbE+XFStg9mx/e82akl+/Vq2yt6ZtuzRqBNWrx+bnIYmh1KLLObcUWBq5vcbMZgHNgHOArpHdngby2aXoAk4D3nHOrQKIFGunA+OrIHvauu46GD4chg3ThKkiItGM/LEHU4tgzN6DqbdqgW/5GjIEevTYbV8zaNDAX1q3LvtrbNrk18UtS7H29dc7bm/dGv35zGCvvcpfrGVmVvCHJHFXrp5qM8sGOgGfAFmRggzn3FIz2y/KIc2AhcXuL4psi/bcvYHeAFlZWeTn55cnWto5/fQDefbZppxxxifst9/G0HFERBLGwoV1yM3N4chfn8anQ5rt3M0Xw/9bGjXylwMPLHkf52Dt2gxWr87gl19qbL/sev+XXzKYO7cG06bVYPXqGmzYUHITWM2aW2nQYAsNGmymYcPNxa79tm2Xhg133K9XbwvV0uBUusLCwoSqJ8pcdJlZPWAi0Nc594uVrbM62k5RZ5lyzo0BxgDk5OS4rl27ljVaWsrOhtdeg48//jX33Rc6jYhIYti6FU44wbf+vPTSPjRt2jV0pCqxceOeWtKqRy61WLkS/vc/+Oor3wpXVBT9+apV8wVieVvVateO7/uurPz8fBKpnihT0WVmNfAFV55z7uXI5mVmtn+klWt/YHmUQxexowsSoDm+G1IqKTvbTyExZgz89a/+DBsRkXT3wAPw3//C3/8OTZuGTlN1atXy76c876moCFavLlv356JF8MUX/vauM20Ul5lZ/kKtYUPSolWtLMpy9qIBTwKznHPF21ReA3oCQyPXr0Y5/C3gbjNrFLnfDbi5UolluwED4Nln4eGH4ZZbQqcREQlr9mz/JfTss6MO3Uo721qzSuvy3NX69SWPVfvxx53vL1jgr3/6qeTVUqpX39GqVpYzP7ddatasmp9DIilLS9exwEXAl2Y2PbJtEL7YesHMLgcWAH8EMLMc4Ern3F+cc6vM7G/Ap5Hj7tw2qF4qr317OPNMGDnSz9+lwZQikq62boVLL/V/B0eP1nQNlVGnDjRr5i9ltXUr/Pxz2VrV5s2DadP87Q0bSn7OevXK36rWoEFif/bmEnAhv5ycHFdQUBA6RlL48EM4/nh46CG49trQaUREwhg+3Lf+5+XBn/4UOo2U1bp1ZSvUil9+2sOkUxkZfrjNxRl59F85mP02LsBatsTujn7malUxs2nOuZxS91PRlfyOPRYWL4a5c6FGjdBpRETia9Ys6NQJuneHiRMTu6VDKm/rVl94lVSUHfBxHn+e0pvaW3eeo40xY2JWeKnoSiOvv+7HMDz7rMYxiEh62bLFf/H87jt/xl5WVuhEElx2dtTVCGjVyvdtxkBZiy6dT5ACzjwTDjnELw2UgDW0iEjMjBgBU6f6E4pUcAlQ4rqbJW6PIxVdKaBaNcjNhS+/hH/9K3QaEZH4+OoruO02OPdcOO+80GkkYbRsWb7tcaSiK0VceCG0aOFbu0REUt2WLXDJJf5stUce0TguKWbIkN1P58/M9NsDU9GVImrU8NNGTJkCH30UOo2ISGwNGwYFBb7g2i/aInSSvnr08IPmW7XCmfmxXDEcRF8eGkifQtau9a2nxx8Pr7wSOo2ISGx8+SUceST87ncwYULoNJLI4rUMkAbSp6G6deG66+DVV/2K9iIiqWbzZt+tuNdeMGpU6DQi5aOiK8Vce62fTXj48NBJRESq3tCh8Nln8OijsO++odOIlI+KrhSzzz7Qq5efs2vhwtBpRESqzhdfwN/+Bhdc4M9YFEk2KrpS0A03+Pm67r8/dBIRkaqxrVuxcWN1K0ryUtGVglq18muPjRnjl0QQEUl2d98N06fDY4/5hY1FkpGKrhQ1YIA/m/Hhh0MnERGpnOnT4a67/Bn/v/1t6DQiFaeiK0UddhicdRaMHOmLLxGRZLRpE/Ts6cerjhwZOo1I5ajoSmG5ub57cezY0ElERCrmrrtgxgwYPdqP5xJJZiq6Uthxx8Gxx8K99/pBqCIiyeSzz/xYrosugrPPDp1GpPJUdKW4gQNh/nzN2iwiyWXjRt+tuN9+8OCDodOIVA0VXSmue3c49FC/EHYCrvgkIhLV3/4GM2fC449Do0ah04hUDRVdKa5aNT+2a+ZMeOON0GlEREpXUOBnnr/kEjjzzNBpRKqOiq40cMEFfiHse+4JnUREZM+2dSs2aaIJniX1qOhKAzVqQP/+8MEH8N//hk4jIlKy22+Hr7/23Yp77RU6jUjVUtGVJi6/3M/irNYuEUlUU6fCsGFw2WVwxhmh04hUPRVdaaJuXbjuOnjtNfjqq9BpRER2tmGD71Zs2hTuuy90GpHYUNGVRq69FjIzYfjw0ElERHZ2220wezY88QQ0bBg6jUhsqOhKI3vvDb16QV4eLFgQOo2IiPfxxzBihP/7dNppodOIxI6KrjRzww3+WmcFiUgiWL/eTw3RvLkvvERSmYquNNOyJfzpTzBmjF+XUUQkpFtugTlz4MknoUGD0GlEYktFVxoaMADWrYOHHw6dRETS2X//6wfNX3EFnHpq6DQisVdq0WVmY81suZnNLLbtcDP7yMy+NLPXzSzq9xMzmxfZZ7qZFVRlcKm4Qw+F//s/GDkS1q4NnUZE0tG6db5bsWVLndwj6aMsLV3jgNN32fYEMNA51x6YBNy0h+NPcs51dM7lVCyixEJuru9eHDs2dBIRSUd//SvMnev/BtWvHzqNSHyUWnQ556YAq3bZ3BaYErn9DnBuFeeSGDv2WDjuOD9wdfPm0GlEJJ188AE88ABcfTWcfHLoNCLxk1HB42YCZwOvAn8EWpSwnwPeNjMHjHbOjSnpCc2sN9AbICsri/z8/ApGk7Lq3n1vBg1qz223zaJbt2Wh44hIGli/vhq9euXQpIlx1lkF5OdvDR1JUlhhYWFC1RPmnCt9J7Ns4B/OucMi9w8GRgJ7A68B1zvn9o5yXFPn3BIz2w/fInZdpOVsj3JyclxBgYaAxVpRERx+ODgHM2ZANZ1WISIx1qePH086eTJ07Ro6jaS6/Px8usbhH5qZTSvLMKoK/TfrnJvtnOvmnDsSGA98V8J+SyLXy/FjvzpX5PUkNqpV82O7vvoK3ngjdBoRSXXvv+8LrmuvVcEl6alCRVek5Qozqwb8FXgsyj51zaz+tttAN3y3pCSQ88/3Zw9pIWwRiaW1a/1C1gccAEOHhk4jEkZZpowYD3wEtDWzRWZ2OXChmX0DzAaWAE9F9m1qZtvaTLKAD83sC2Aq8E/n3JuxeBNScTVqwI03wocfwn/+EzqNiKSqgQPh++/hqaegbt3QaUTCKNOYrnjTmK74WrsWWrWCLl3gtddCpxGRVDN5sj9LsU8ff9aiSLykxJguSS1168L118Prr8NMdQCLSBUqLPTdigceCHffHTqNSFgqugSAa66BzEzNDC0iVWvAAJg/33crZmaGTiMSloouAWDvvaF3b3juOViwIHQaEUkF770Hjz4Kffv6yZhF0p2KLtnuhhv89X33hc0hIslvzRq4/HJo0wbuuit0GpHEoKJLtmvRAnr0gMcf9+syiohU1E03+VZzdSuK7KCiS3YyYACsWwejRoVOIiLJ6p13YPRo6N/fnxUtIp6KLtnJIYfA2Wf7WaPXrg2dRkSSzS+/+G7Ftm3hzjtDpxFJLCq6ZDe5ubBqFTzPj2vuAAAgAElEQVT5ZOgkIpJs+veHxYth3DioUyd0GpHEoqJLdtOlCxx/PNx7L2zeHDqNiCSLt96CJ57wq1wcc0zoNCKJR0WXRJWb6wfBPv986CQikgxWr4a//AXatYM77gidRiQxqeiSqLp3h8MO8wthFxWFTiMiie6GG2DJEt+tWLt26DQiiUlFl0Rl5lu7vvoK3nij9P1FJH298QaMHev/ZnTuHDqNSOJS0SUlOv98vxD20KGhk4hIovrpJ+jVCw49FG67LXQakcSmoktKVKOGHxD7n//Ahx+GTiMiiahfP1i2zHcr1qoVOo1IYlPRJXt02WWwzz5+bJeISHH/+Ac8/TQMHAg5OaHTiCQ+FV2yR5mZcP31/o/rzJmh04hIovjpJ+jdG9q3h1tuCZ1GJDmo6JJSXXMN1K0Lw4aFTiIiiaJPH1i+XN2KIuWhoktK1bix/0b73HMwf37oNCIS2muvwd//DoMHwxFHhE4jkjxUdEmZ9Ovnp5G4777QSUQkpJUr4Yor4PDDfdElImWnokvKpEUL+POf4fHH4ccfQ6cRkVCuv97/DRg3DmrWDJ1GJLmo6JIyGzAA1q+HUaNCJxGRECZN8sMM/vpX6NgxdBqR5KOiS8qsXTs45xx46CFYuzZ0GhGJpx9/hCuv9MXWoEGh04gkJxVdUi65ubBqFTzxROgkIhJP113np4l4+mk/cbKIlJ+KLimXX/8aTjgB7r0XNm8OnUZE4mHiRHj+ebj1VujQIXQakeSlokvKLTcXFi6E8eNDJxGRWFuxAq66yk8NkZsbOo1IclPRJeV2xhl+Fup77oGiotBpRCSWrrkGfv5Z3YoiVUFFl5Sbmf/G+/XX8M9/hk4jIrHywgvw4otw++1w2GGh04gkv1KLLjMba2bLzWxmsW2Hm9lHZvalmb1uZg1KOPZ0M5tjZt+a2cCqDC5hnX8+ZGfD0KGhk4hILCxf7lu5cnL8dDEiUnllaekaB5y+y7YngIHOufbAJOCmXQ8ys+rAw8AZwCHAhWZ2SKXSSsLIyID+/eG//4UPPwydRkSqknNw9dXwyy++WzEjI3QikdRQatHlnJsCrNplc1tgSuT2O8C5UQ7tDHzrnPveObcJeB44pxJZJcFcdhnss49au0RSzYQJ/ozFO++EQ/RVWaTKVHRM10zg7MjtPwItouzTDFhY7P6iyDZJEZmZ0KePH9f15Zeh04hIVfjf/3y3YufOvjVbRKpORRuNLwNGmtmtwGvApij7WJRtrqQnNLPeQG+ArKws8vPzKxhN4qlDhwxq1/41/fuvYNCg2aHjiEglOAe33nooa9bszdVXF/Dhh+tCRxKplMLCwoSqJypUdDnnZgPdAMysDXBmlN0WsXMLWHNgyR6ecwwwBiAnJ8d17dq1ItEkgKuvhgcfbMLjjzehVavQaUSkop57zo/RHDYMevbsHDqOSKXl5+eTSPVEhboXzWy/yHU14K/AY1F2+xQ4yMxam1lN4AJ8q5ikmH79oFo1P0u9iCSnpUvh2mvhmGPghhtCpxFJTWWZMmI88BHQ1swWmdnl+DMRvwFm41uvnors29TM3gBwzm0BrgXeAmYBLzjnvorN25CQmjeHP//Zr8e4YkXoNCJSXs7BFVfA+vUwbhxUrx46kUhqKrV70Tl3YQkPPRhl3yVA92L33wDeqHA6SRo33QRPPQWjRsEdd4ROIyLl8eyz8PrrvrW6bdvQaURSl2aklyrRrh389rfw0ENQWBg6jYiU1ZIlcP31cOyx/mxkEYkdFV1SZXJz4aeffDejiCQ+56B3b9i4EcaOVbeiSKyp6JIqc8wxcOKJvotiU7RJREQkoTzzjJ9n7+67oU2b0GlEUp+KLqlSubmwaBGMHx86iYjsyeLFvjvx+ON996KIxJ6KLqlSp58OHTrAPfdAUVHoNCISjXPQq5dvkR471k/5IiKxp181qVJmvrVr1iz4xz9CpxGRaJ56Cv71L//l6MADQ6cRSR8quqTKnXceZGf7hbBdiQs/iUgICxf6CY1PPNGvsSgi8aOiS6pcRgbceCN89JFfUkREEsO2bsWtW9WtKBKCfuUkJi69FPbd17d2iUhiePJJeOst3614wAGh04ikHxVdEhOZmf6MqDfegBkzQqcRkQUL/JqKJ50EV10VOo1IelLRJTFzzTVQrx4MGxY6iUh6cw4uv9yfUfzkk+pWFAlFv3oSM40a+UV0n38e5s0LnUYkfY0ZA+++CyNGQOvWodOIpC8VXRJT/fr5b9X33hs6iUh6mjfPn9hyyin+S5CIhKOiS2KqWTO46CLfpbFiReg0IumlqMh3K4L/HTQLm0ck3anokpi76SbYsAEeeih0EpH0Mno0/PvfvqW5VavQaURERZfE3MEHw29/C6NGQWFh6DQi6eGHH/wXnt/8xs/NJSLhqeiSuMjNhZ9+gscfD51EJPUVFcFll/nxlE88oW5FkUShokvi4uijoWtX382xaVPoNCKp7ZFHID8f7r8fWrYMnUZEtlHRJXGTmwuLF8Nzz4VOIpK6vvvO/66dfrpv7RKRxKGiS+LmtNPg8MP9EiRFRaHTiKSebd2KNWr4rnx1K4okFhVdEjdm/hv47Nnw+uuh04iknlGjYMoU363YvHnoNCKyKxVdEld//KOfEXvoUL80iYhUjblzYeBA6N4dLrkkdBoRiUZFl8RVRoafHfvjj+GDD0KnEUkNW7fCpZdCzZp+yR91K4okJhVdEneXXgr77utbu0Sk8kaOhP/8x183axY6jYiUREWXxF2dOtCnD/zrXzBjRug0Isntm29g0CA46yy/5JaIJC4VXRLE1VdDvXr+TEYRqZitW/34rTp1/JI/6lYUSWwquiSIRo3giitgwgS/XImIlN8DD8BHH/luxaZNQ6cRkdKo6JJg+vXzy5Tce2/oJCLJZ/ZsGDwYzjkHevQInUZEyqLUosvMxprZcjObWWxbRzP72Mymm1mBmXUu4ditkX2mm9lrVRlckl+zZnDxxfDkk7B8eeg0IsljW7di3brw2GPqVhRJFmVp6RoHnL7LtmHAHc65jsCtkfvRrHfOdYxczq54TElVN90EGzfCQw+FTiKSPO69Fz75xE+G2qRJ6DQiUlalFl3OuSnAql03Aw0itxsCS6o4l6SJtm3hd7+Dhx+GNWtCpxFJfF9/Dbfe6n9vLrggdBoRKY+MCh7XF3jLzEbgC7cuJexX28wKgC3AUOfcKyU9oZn1BnoDZGVlkZ+fX8FokmxOPbU+L798JLm533LeeYtCxxFJWFu3Gtde24laterw5z9P5f33N4eOJJLQCgsLE6qeMFeGtVjMLBv4h3PusMj9kcD7zrmJZnYe0Ns5d2qU45o655aY2QHAv4FTnHPflfZ6OTk5rqCgoHzvRJLaySf7+Ya+/97Pqi0iuxs6FG6+GZ5/Hs4/P3QakcSXn59P165dY/46ZjbNOZdT2n4VPXuxJ/By5PaLQNSB9M65JZHr74F8oFMFX09SXG4uLF4MeXmhk4gkppkz4bbb4A9/gPPOC51GRCqiokXXEuDEyO2Tgbm77mBmjcysVuT2PsCxwNcVfD1Jcd26QceOMGwYFBWFTiOSWDZv9mcrNmjgxz/qbEWR5FSWKSPGAx8Bbc1skZldDvQC7jWzL4C7iYzFMrMcM3sicmg7oCCyz2T8mC4VXRKVmW/tmj0bXtPkIiI7GTYMpk2DRx6B/fYLnUZEKqpMY7riTWO60tOWLf5sxn339bNs69u8CHz5JRx5pD9bccKE0GlEkkuqjOkSqXIZGXDjjX7+oSlTQqcRCW/zZujZ0y+b9fDDodOISGWp6JKEcsklvvtk6NDQSUTCGzoUPv8cHn0U9tkndBoRqSwVXZJQ6tSBPn3gzTfhiy9CpxEJ54sv4M474cIL4fe/D51GRKqCii5JOFdfDfXrwz33hE4iEsamTb7Vd++9tUSWSCpR0SUJZ6+94Ior/KDh778PnUYk/u6+G6ZPh9GjfeElIqlBRZckpH79/MD6e+8NnUQkvj7/HIYMgR494JxzQqcRkaqkoksSUtOmcNFFMHYsLF8eOo1IfGzrVtxnHxg5MnQaEalqKrokYd10E2zcqP98JH3cdRfMmAFjxkDjxqHTiEhVU9ElCattW3/W1sMPw5o1odOIxNa0aX4s18UXw//9X+g0IhILKrokoeXmws8/+2/+Iqlq40bfrZiVBQ88EDqNiMSKii5JaEcdBSefDPfd5/9jEklFd94JM2f6LxeNGoVOIyKxoqJLEl5uLixZAnl5oZOIVL1PP/Vz0l1yCZx5Zug0IhJLKrok4f3mN9CpEwwbBkVFodOIVJ0NG3yx1aQJ3H9/6DQiEmsquiThmfnWrjlz4NVXQ6cRqTp33AFffw2PP+4nBRaR1KaiS5LCuefCr37lFwB2LnQakcr75BPfenv55XDGGaHTiEg8qOiSpJCRATfeCFOnwvvvh04jUjnbuhWbNtWqCyLpREWXJI1tp9RrIWxJdrfeCrNnw5NPQsOGodOISLyo6JKkUbs29OkDb77pFwMWSUYffeRbt3r1gm7dQqcRkXhS0SVJ5aqroH59PxZGJNmsX+9bbJs3hxEjQqcRkXhT0SVJZa+94MorYcIE+P770GlEyueWW+Cbb3y3YoMGodOISLyp6JKk07evH1ivlgJJJv/5j19Z4cor4dRTQ6cRkRBUdEnSadrULwr81FOwbFnoNCKlW7cOLr0UWrZU17hIOlPRJUnpppv8WowjR4ZOIlK6wYNh7lwYO9aPSRSR9KSiS5JSmzbw+9/DI4/AL7+ETiNSsg8+gAcfhKuv9ou3i0j6UtElSSs3F37+GcaMCZ1EJLq1a323Yna25pcTERVdksSOOgpOOcUvFLxxY+g0IrsbNAi++853K9arFzqNiISmokuSWm4uLFkCzz4bOonIzt5/3485vO466No1dBoRSQQquiSpnXoqHHEEDB8OW7eGTiPiFRbCZZf5Rdr/3/8LnUZEEkWZii4zG2tmy81sZrFtHc3sYzObbmYFZta5hGN7mtncyKVnVQUXATDzrV1z5sCrr4ZOI+INHAg//OCnNalbN3QaEUkUZW3pGgecvsu2YcAdzrmOwK2R+zsxs8bAbcDRQGfgNjNrVOG0IlGce65vUbjnHnAudBpJd5Mnw8MP+3VCjz8+dBoRSSRlKrqcc1OAVbtuBrYtZNEQWBLl0NOAd5xzq5xzPwHvsHvxJlIp1av7ebumToX8/NBpJJ1t61Y86CAYMiR0GhFJNBmVOLYv8JaZjcAXb12i7NMMWFjs/qLItt2YWW+gN0BWVhb5+t9TyqF162o0anQMubmFDBs2I3QcSVP3338Q8+c35cEHP2fqVE0gJxJaYWFhQtUTlSm6rgL6Oecmmtl5wJPAriuKWZTjonYAOefGAGMAcnJyXFed7iPlNGAA3HxzYxo27EqnTqHTSLp57z147TW44Qa47rojQscRESA/P59Eqicqc/ZiT+DlyO0X8WO2drUIaFHsfnOid0OKVNpVV0GDBlrbTuLvl198t2KbNnDXXaHTiEiiqkzRtQQ4MXL7ZGBulH3eArqZWaPIAPpukW0iVa5hQ7jySnjhBT8hpUi83HQTLFoE48ZBnTqh04hIoirrlBHjgY+Atma2yMwuB3oB95rZF8DdRMZjmVmOmT0B4JxbBfwN+DRyuTOyTSQm+vaFjAy4997QSSRdvP22X4qqf3/49a9DpxGRRGYuAc+xz8nJcQUFBaFjSJLq3RueeQbmz4esrNBpJJWtXg3t2/u5uD7/HGrXDp1IRIqL15guM5vmnMspbT/NSC8p56abYNMmvwSLSCzdeCMsXuy7FVVwiUhpVHRJyjnoID9h6sMP+wHOIrHw5pvwxBO+yD/66NBpRCQZqOiSlJSb67t+Ro8OnURS0c8/w1/+AoccArffHjqNiCQLFV2SknJy4JRT4P77YePG0Gkk1dxwA/zvf+pWFJHyUdElKWvgQFi6FP7+99BJJJW88YZfyDo3F446KnQaEUkmKrokZZ1yChx5JAwfDlu3hk4jqeCnn6BXLzjsMLj11tBpRCTZqOiSlGXmWyO++QZeeSV0GkkF/frBsmW+W7FWrdBpRCTZqOiSlPb738OBB8I990ACTkknSeT11+Hpp+Hmm30LqohIeanokpRWvbo/pf/TT2Hy5NBpJFmtWgVXXAEdOsAtt4ROIyLJSkWXpLyLL4YmTXxrl0hF9OkDK1b4bsWaNUOnEZFkpaJLUl7t2n5Nxrffhs8+C51Gks2rr8Kzz8LgwdCpU+g0IpLMVHRJWrjySmjQAIYNC51EksnKlb5b8fDDYdCg0GlEJNmp6JK00LAhXHUVvPgifPCBJkyVsrn+el94Pf20uhVFpPIyQgcQiZe+fWHUKDjhBKhWDVq3hrZt/eXgg3fczsry001Ieps0CZ57Du64w7d0iYhUloouSRtNmsDMmfDf/8Ls2TBnjr/8+9+wYcOO/Ro02LkI21aUHXiglnxJFz/+6LukO3XyU0SIiFQFFV2SVrKz/aW4oiJYuHBHEbatIJs8eeclhMz8sbu2jLVtC/vvr9axVHLttX72+XffhRo1QqcRkVShokvSXrVq0KqVv3TrtvNjhYV+RvttBdm2omzKFFi3bsd+9etH76o86CCoUye+70cq56WXYMIEuOsuaN8+dBoRSSUqukT2oF49OOIIfymuqAgWL965ZWzOHD9IPy9vx35mvpjbtauybVto2lStY4lmxQq4+mo/43xubug0IpJqVHSJVEC1atCihb+ceurOj61dC3Pn7t5d+eGH/rFt6tWDNm12bx1r0wYyM+P7fsS75hpYvdpPgpqhv44iUsX0Z0XSR16en+FywQJo2RKGDIEePar8ZerWhY4d/aU452DJkp1bxubM8QP7n39+57UhW7aM3l3ZvLlax2LlhRf8lCJ33w2HHRY6jYikIhVdkh7y8qB37x0DsebP9/chJoVXNGbQrJm/nHLKzo+tX7+jdax4Ufb007BmzY79MjN37qrcVpS1aeOLPamYZct8t+JRR/m1OkVEYkFFl6SHwYN3HvkO/v7gwXEruvakTh2/mHKHDjtvdw6WLt19IP8nn/jB3sVbx5o3j35mZYsWvjtUonPOF1xr1qhbUURiS39eJD0sWFC+7QnCzA+4b9oUTjpp58c2bIBvv929u/Lvf4dfftmxX506O8aOFS/K2rTxZ12muwkT4OWX/YLohxwSOo2IpDIVXZIeWrb0XYrRtiep2rX92KNdxx8557vLdh3IP22anw6hqGjHvk2bRp8ItmXL9Ggd+9///OD5o4+G/v1DpxGRVKeiS9LDkCE7j+kCP0BqyJBwmWLEzM++36QJnHjizo9t3Ohbx3YtyMaPh59/3rFf7dp+jrFo3ZUNGsT3/cSKc37W+bVrfbdi9eqhE4lIqlPRJelh27itOJy9mMhq1YJDD/WX4pzzc1Tt2lU5fbpfg3Dr1h377r9/9HnHWrVKrsLluefg1Vdh+HD/HkREYs1c8ZG4CSInJ8cVFBSEjiEiwKZN8N13u59ZOXu2Xypnm1q1/PqUu7aMtW0Le+0VLn80S5f6wvPgg/2EtslULIpI2eXn59O1a9eYv46ZTXPO5ZS2n1q6RGSPataEdu38pTjn/MLQu55Z+eWX8MorO7eOZWVFn3csOzv+Zws6B1dc4afpULeiiMRTqX/uzGwscBaw3Dl3WGTbBKBtZJe9gJ+dcx2jHDsPWANsBbaUpQoUkeRgBvvu6y/HHbfzY5s3w/ff795d+fLLsHLljv1q1vStY9G6Kxs1ik3uZ5+F11+H++7zZ3CKiMRLWb5jjgNGAc9s2+CcO3/bbTO7F1i9h+NPcs79WNGAIpJ8atTYUUTtauXK3Qfyz5rlC6EtW3bst+++0QfyH3BAxVvHliyB66+HY4/11yIi8VTqny7n3BQzy472mJkZcB5wctXGEpFUtffe0KWLvxS3eTP88MPu3ZWvvuoH+W9Towb86lfRuyv33rvk13XOn8C6cSM89ZS6FUUk/io7muJ4YJlzbm4JjzvgbTNzwGjn3JiSnsjMegO9AbKyssjPz69kNBFJRvXrQ06Ov2yzZk0GCxdmsmBBJgsX1mHhwkymT8/kn/+sw5YtOyYUa9BgMy1brqNFC3/Zdrtp0w28+24W//znwVxzzVwWL17M4sUB3pyIxFVhYWFC1RNlOnsx0tL1j21juoptfxT41jl3bwnHNXXOLTGz/YB3gOucc1NKez2dvSgiZbFlC8ybt3t35Zw5foLYbTIy/Bi0Y46B/Pz0mPhVRFLo7EUzywB+DxxZ0j7OuSWR6+VmNgnoDJRadImIlEVGhh+If+CBcOaZOz/28887d1UuWgR33KGCS0TCqUz34qnAbOfcomgPmlldoJpzbk3kdjfgzkq8nohIme21l1/e5+ijQycREfFK/c5nZuOBj4C2ZrbIzC6PPHQBMH6XfZua2RuRu1nAh2b2BTAV+Kdz7s2qiy4iUkZ5eX5SsGrV/HVeXuhEIpKGynL24oUlbL8kyrYlQPfI7e+BwyuZT0SkcvLydl53c/58fx/SbhkoEQlLoxtEJLUNHrzzQufg7w8eHCaPiKQtFV0iktoWLCjfdhGRGFHRJSKprWXL8m0XEYkRFV0iktqGDIHMzJ23ZWb67SIicaSiS0RSW48eMGYMtGrlZ0ht1crf1yB6EYmzyi4DJCKS+Hr0UJElIsGppUtEREQkDlR0iYiIiMSBii4RERGROFDRJSIiIhIHKrpERERE4kBFl4iIiEgcqOgSERERiQMVXSIiIiJxoKJLREREJA5UdImIiIjEgTnnQmfYjZmtAObH+GUaAqtj/BqSmPTZpy999ulJn3v6agksiMPrtHLO7VvaTglZdMWDmY1xzvUOnUPiT599+tJnn570uacvM1tRlmIoXtK5e/H10AEkGH326UuffXrS556+fg4doLi0bekSERGR1GZmBc65nNA5tknnli4RERFJbWNCByhOLV0iIiIicZAWLV1mNtbMlpvZzGLbbjezxWY2PXLpHjKjxEa0zz6y/Tozm2NmX5nZsFD5JDZK+J2fUOz3fZ6ZTQ+ZUWKjhM++o5l9HPnsC8ysc8iMkr7SougCxgGnR9l+v3OuY+TyRpwzSXyMY5fP3sxOAs4BOjjnDgVGBMglsTWOXT5359z5237fgYnAyyGCScyNY/e/98OAOyKf/a2R+yJxlxZFl3NuCrAqdA6JvxI++6uAoc65jZF9lsc9mMTUnn7nzcyA84DxcQ0lcVHCZ++ABpHbDYElcQ0lcVFCK+fhZvaRmX1pZq+bWYM9PUespUXRtQfXmtmMyAfVKHQYiZs2wPFm9omZvW9mR4UOJHF1PLDMOTc3dBCJm77AcDNbiG/ZvjlwHomNcezeyvkEMNA51x6YBNwU71DFpXPR9SjwK6AjsBS4N2wciaMMoBFwDP4X8IVI64ekhwtRK1e6uQro55xrAfQDngycR2KghFbOtsCUyO13gHPjGmoXaVt0OeeWOee2OueKgMcBDaxMH4uAl503FSgC9gmcSeLAzDKA3wMTQmeRuOrJjjF8L6K/9+lkJnB25PYfgRYBs6Rv0WVm+xe7+zv8ByPp4RXgZAAzawPUBH4Mmkji5VRgtnNuUeggEldLgBMjt08G1LWcPi4DrjGzaUB9YFPIMBkhXzxezGw80BXYx8wWAbcBXc2sI36A5TzgimABJWZK+OzHAmMjgy03AT2dJqxLKdE+d+fck8AFqGsxpZXwO98LeDDS0rkB0DqMacI5NxvoBtu/ZJ8ZMo8mRxUREZGUYGbZwD+cc4dF7u/nnFtuZtXwA+3znXNjQ+VL2+5FERERSR2RVs6PgLZmtsjMLgcuNLNvgNn4buangmZUS5eIiIhI7KmlS0RERCQOUrroMrPmZvaqmc01s+/M7EEzq7mH/fuaWWY8M4qIiEh6SNmiKzLZ5cvAK865g/CzkNcDhuzhsL6Aii4RERGpcik7psvMTsGfJn5CsW0NgB+AlsAdwGn4KSMeBwy/PMQc4Efn3ElxDy0iIiIpK5Xn6ToUmFZ8g3PuFzNbAPwFaA10cs5tMbPGzrlVZnYDcJJzThNlioiISJVK2e5FfMtVtGY8A04AHnPObQFwzu26VpOIiIhIlUrlousrIKf4hkj3YgtKLshEREREYiKVi673gEwzuxjAzKoD9+JnpH0buDKyJARm1jhyzBr82kwiIiIiVSpli67IWnq/A/5oZnOBb/Brbg0CngAWADPM7AvgT5HDxgD/MrPJASKLiIhICkvZsxdFREREEknKtnSJiIiIJBIVXSIiIiJxkDJFl5m1MLPJZjbLzL4ysz6R7Y3N7J3IUkDvmFmjyPaDzewjM9toZjfu8lz9Is8x08zGm1ntEO9JREREUkfKFF3AFqC/c64dcAxwjZkdAgwE3ossBfRe5D7AKuB6/Cz025lZs8j2HOfcYUB14IL4vAURERFJVSlTdDnnljrnPovcXgPMApoB5wBPR3Z7GvhtZJ/lzrlPgc1Rni4DqBOZUiITWBLj+CIiIpLiUqboKs7MsoFOwCdAlnNuKfjCDNhvT8c65xbjW78WAEuB1c65t2OZV0RERFJfyhVdZlYPmAj0dc79UoHjG+Fbx1oDTYG6Zvbnqk0pIiIi6Salii4zq4EvuPKccy9HNi8zs/0jj+8PLC/laU4FfnDOrXDObQZeBrrEKrOIiIikh5QpuszMgCeBWc65+4o99BrQM3K7J/BqKU+1ADjGzDIjz3kKfnyYiIiISIWlzIz0ZnYc8AHwJVAU2TwIP67rBaAlvqD6o3NulZk1AQqABpH9C4FDnHO/mNkdwPn4MyI/B/7inNsYz/cjIiIiqSVlii4RERGRRJYy3YsiIiIiiUxFl4iIiEgcqOgSERERiQMVXSIiIiJxoKJLREREJA5UdIlISjGz283sxj08/lszOySemUREQEWXiGevwisAAAGoSURBVKSf3wIqukQk7jRPl4gkPTMbDFwM/P/27lg1qiAK4/j/SKIEsmBj4TsIBlKkEEHsBUWtREgT8Ql8ABF8AAkJWGRLQUGQNBY29goiiKAgoiAhCInBgI2fxU4RJJ1kZHf/v/KcuZcz3ce9A/MF2AZeAbvALeA48BG4CSwAm623C1xtr1gFTgH7wEqS9z3nlzQdDF2SxlpVLQJDYAmYAV4D68BGku9tzT1gK8mDqhoCm0metN4L4HaSD1W1BNxPcrH/TiRNupn/PYAk/aPzwNMk+wBV9azVz7SwdRKYB57//WBVzTO60P7x6KpVAE4c+cSSppKhS9IkOOyT/RC4nORNVS0DFw5ZcwzYSbJwdKNJ0ogH6SWNu5fAlaqaq6oBcKnVB8C3qpoFbhxYv9d6JPkBfKqq6wA1crbf6JKmiWe6JI29AwfpPwNfgXfAT+BOq70FBkmWq+oc8BD4BVwDfgNrwGlgFniU5G73TUiaeIYuSZKkDvy9KEmS1IGhS5IkqQNDlyRJUgeGLkmSpA4MXZIkSR0YuiRJkjowdEmSJHVg6JIkSergD237n2nRk6JgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_merged = pd.DataFrame({\n",
    "    'Calculated': pd_calcs['VIX'],\n",
    "    'Actual': df_vix,\n",
    "})\n",
    "df_merged.plot(figsize=(10, 6), grid=True, style=['b', 'ro']);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
